Some recent works [1]}, [2]}, [3]} have introduced designs of unrolled (or cross-domain) neural networks to tackle the problem of Cartesian MRI reconstruction.
However, to the best of our knowledge, only one [4]} has tried to use the same ideas for non-Cartesian data.
The difference with our work is that they considered only one trajectory, variable-density sampling, and because this trajectory was particular they did not need to introduce DC.
Another difference with the work conducted in [4]} is that we use emulated single-coil data, and do not synthesize any phase information.
The data that we handle is also much bigger (\(320 \times 320\)  target images in our case, and \(192 \times 192\)  in theirs).
